TY - JOUR
T1 - Modeling strategies to analyse longitudinal biomarker data
T2 - An illustration on predicting immunotherapy non-response in non-small cell lung cancer
AU - van Delft, Frederik A
AU - Schuurbiers, Milou
AU - Muller, Mirte
AU - Burgers, Sjaak A
AU - van Rossum, Huub H
AU - IJzerman, Maarten J
AU - Koffijberg, Hendrik
AU - van den Heuvel, Michel M
N1 - © 2022 The Author(s).
PY - 2022/10/1
Y1 - 2022/10/1
N2 - Serum tumor markers acquired through a blood draw are known to reflect tumor activity. Their non-invasive nature allows for more frequent testing compared to traditional imaging methods used for response evaluations. Our study aims to compare nine prediction methods to accurately, and with a low false positive rate, predict progressive disease despite treatment (i.e. non-response) using longitudinal tumor biomarker data. Bi-weekly measurements of CYFRA, CA-125, CEA, NSE, and SCC were available from a cohort of 412 advanced stage non-small cell lung cancer (NSCLC) patients treated up to two years with immune checkpoint inhibitors. Serum tumor marker measurements from the first six weeks after treatment initiation were used to predict treatment response at 6 months. Nine models with varying complexity were evaluated in this study, showing how longitudinal biomarker data can be used to predict non-response to immunotherapy in NSCLC patients.
AB - Serum tumor markers acquired through a blood draw are known to reflect tumor activity. Their non-invasive nature allows for more frequent testing compared to traditional imaging methods used for response evaluations. Our study aims to compare nine prediction methods to accurately, and with a low false positive rate, predict progressive disease despite treatment (i.e. non-response) using longitudinal tumor biomarker data. Bi-weekly measurements of CYFRA, CA-125, CEA, NSE, and SCC were available from a cohort of 412 advanced stage non-small cell lung cancer (NSCLC) patients treated up to two years with immune checkpoint inhibitors. Serum tumor marker measurements from the first six weeks after treatment initiation were used to predict treatment response at 6 months. Nine models with varying complexity were evaluated in this study, showing how longitudinal biomarker data can be used to predict non-response to immunotherapy in NSCLC patients.
UR - http://www.scopus.com/inward/record.url?scp=85139734744&partnerID=8YFLogxK
U2 - 10.1016/j.heliyon.2022.e10932
DO - 10.1016/j.heliyon.2022.e10932
M3 - Article
C2 - 36254284
SN - 2405-8440
VL - 8
JO - Heliyon
JF - Heliyon
IS - 10
M1 - e10932
ER -